Simple Prediction Method Research Articles

Development of a simplified thermal performance prediction method based on free-running temperatures and building envelope characteristics

Various thermal prediction methods and tools for conceptual building design have been rapidly developed in the past decades. However, most of these methods have some drawbacks to obsess their applications, such as heavily relying on very detailed information of building envelope components, and their thermal conductivity data with complicated computation models or formulas; and difficulties for architects to understand and judge the outcomes of such predictions in a conceptual design. Therefore, this research targets on developing a simplified method to predict the integrated thermal performance of a building with simplified envelope parameters and hourly free-running temperatures in the conceptual design stage. It categorizes the scattered design parameters into three combined thermal characteristic indexes and applies a serials of corresponding quasi-steady calculation methods for building thermal processes to predict hourly free-running temperatures. The interrelationships between these three combined indexes and two overall thermal performance indicators are verified via a sensitivity analysis, and are presented in two charts, which can easily be adopted in a conceptual building design. This method has been validated in an experimental case study. It can help architects to quickly find the thermal design performance of a designed building in the conceptual design stage.

Prediction and Analysis of Strawberry Moisture Content based on BP Neural Network Model

Background: Moisture content is one of the most important indicators for the quality of fresh strawberries. Currently, several methods are usually employed to detect the moisture content in strawberry. However, these methods are relatively simple and can only be used to detect the moisture content of single samples but not batches of samples. Besides, the integrity of the samples may be destroyed. Therefore, it is important to develop a simple and efficient prediction method for strawberry moisture to facilitate the market circulation of strawberry. Objective: This study aims to establish a novel BP neural network prediction model to predict and analyze strawberry moisture. Methods: Toyonoka and Jingyao strawberries were taken as the research objects. The hyperspectral technology, spectral difference analysis, correlation coefficient method, principal component analysis and artificial neural network technology were combined to predict the moisture content of strawberry. Results: The characteristic wavelengths were highly correlated with the strawberry moisture content. The stability and prediction effect of the BP neural network prediction model based on characteristic wavelengths are superior to those of the prediction model based on principal components, and the correlation coefficients of the calibration set for Toyonaka and Jingyao respectively reached up to 0.9532 and 0.9846 with low levels of standard deviations (0.3204 and 0.3010, respectively). Conclusion: The BP neural network prediction model of strawberry moisture has certain practicability and can provide some reference for the on-line and non-destructive detection of fruits and vegetables.

Practical prediction method on frost heave of soft clay in artificial ground freezing with field experiment

Artificial ground freezing (AGF) is a method of paramount importance for underground construction in soft soil area. There were numerous studies concerned on field observations and the theoretical formulations on frost heaves. However, the field observations and validity of the theoretical models from practical points were lack. This paper focused on a more accurate practical prediction method of frost heave by multilayer field experiments and segregation potential (SP) model, for a strict deformation requirement AGF project in a non-stopping airport during construction. Thus, a large-scale area (3.5 times than actual frozen curtain) field experiment of multilayered temperatures and displacements was conceived and developed, to evaluate the freezing effect and deformation characteristics. Temperature variations show that the additional freezing tubes at the tunnel opening can only achieve faster cooling rate initially but could not descend the final stable temperature, and so substantially increasing freezing tubes at the tunnel opening may not be best choice to keep excavation safe. Multilayered temperatures and displacements show that deformation of multi-layers is directly relevant to the position of frost front. A simplified frost heave prediction method using segregation potential concept is proposed based on field experimental data. The temperature gradient and frost front function have been calculated based on field monitoring results and served for the model computation. This prediction method was further validated by both other’s published and our field experimental data. 2–8% relative errors surrounding the axis of frozen curtain prove the applicability of this practical prediction model is much well. This paper provides valuable reference for urban tunneling under extreme conditions such as non-stopping airport or adjacent to existing structures.

Original Method to Predict and Monitor Carbon Deposition on Ni-Based Catalysts During Dry Reforming of Methane

Although the catalytic reforming of methane with carbon dioxide, also known as dry reforming, has recently attracted considerable attention due, the one hand, to the simultaneously utilization of two greenhouses with high environmental impact and, the other hand, to produce syngas with a lower H2/CO ratio than those available from steam reforming and partial oxidation of methane, which is preferred for the synthesis of valuable oxygenated chemicals and long-chain hydrocarbons, has not yet been commercialized. Low-cost Ni-based catalysts with high activity were developed. However, due to their major drawbacks, which are carbon formation and Ni particles sintering at the high temperatures requires by the endothermic reaction, their industrial applicability is limited. What is more, access to advanced characterization techniques is needed to monitor coke deposition and to assess the efficiency of the dry reforming process, access which can be challenging for small and on-site laboratories. The focus of this current study is on the development and testing of a simple carbon deposition prediction method, easily accessible, based on the comparison of the gas composition measured at the exit of the reactor with the theoretical one, calculated based on the process thermodynamics. Trustworthy results, confirmed by SEM and TGA/DSC measurements, were obtained when the method was applied for the monometallic, Ni/SBA-15, prepared by wet impregnation, and bimetallic, Ni-Co/SBA-15 and Ni-La/SBA-15 samples, prepared by impregnation but co-impregnation as well, with different metal loading. The dry reforming process was performed at four temperatures, 550, 600, 650 and 700°C, respectively.

Toward link prediction in directed social networks based on common interest and local community

Link prediction has been widely applied in social network analysis. Existing studies on link prediction assume the network to be undirected, while most realistic social networks are directed. In this paper, we design a simple but effective method of link prediction in directed social networks based on common interest and local community. The proposed method quantifies the contributions of neighbors with analysis on the information exchange process among nodes. It captures both the essential motivation of link formation and the effect of local community in social networks. We validate the effectiveness of our method with comparative experiments on nine realistic networks. Empirical studies show that the proposed method is able to achieve better prediction performance under three standard evaluation metrics, with great robustness on the size of training set.

Simple models for predicting water loss of bread during baking

AbstractThe baking process has received quite attention during the last three decades, due to its economic and industrial relevance, and since it is associated with staple foods like bread. Because of the composition and structure of dough, a series of complex, simultaneous and coupled phenomena occur in the product inside the oven, which make very difficult the design, control and optimization of the process. For these tasks, the use of mathematical models appears as an adequate solution, although they still represent a complex approach for industrial purposes and their use is limited. In this paper, we focus on prediction of water loss, which is relevant for different aspects of the process, for example, texture and sensory properties, economic impact, energy demand. Two simple models are developed based on knowledge about transport phenomena of bread baking. Both methods involve the estimation of a mass transfer coefficient, which can be related to operating conditions. Good prediction performance is found in both cases, in a common range of baking conditions. Finally, the use of water loss as a design and control variable of the process is discussed, and we propose the construction of a baking diagram with moisture loss as independent variable.Practical applicationsOverall, there is a lack of simple and accurate prediction methods for baking, in contrast with other traditional food process operations, since the complexity of the process makes very difficult the task of developing user‐friendly models. This work proposes two simple models to predict water loss variation during bread baking, based on knowledge about transport phenomena of the process. Implementation of the proposed methods needs simple adjustment of parameters, which can be related to operating conditions (oven temperature and apparent heat transfer coefficient). Besides, the construction of a baking diagram is discussed.

Predictive value of Prostate Specific Antigen variations in the last week of salvage radiotherapy for biochemical recurrence of prostate cancer after surgery: A practical approach.

BackgroundAbout a third of patients who underwent radical prostatectomy for prostate cancer (Pca) develop a biochemical failure (BF) within 10 years from surgery, and about a half of them receive salvage radiation therapy (SRT). Factors to predict risk to relapse after SRT are still lacking. Dynamic models, based on the assessment of changes in Prostate Specific Antigen (PSA) postsurgery seem to show good reliability.AimsThe goal of the study was to identify a simple analytical method for the postsalvage radiation therapy biochemical failure (post‐SRTBF) prediction before the end of the SRT, regardless of the PSA value at the beginning of the treatment (PSA start), measuring the PSA values at the start and 1 week before the end of SRT.MethodsIn a series of 83 patients treated with SRT for BF of Pca we measured PSA values at the first day and 1 week before the end of SRT. These values were used to define an analytical method for the post‐SRTBF prediction.ResultsPSA value in patients without post‐SRTBF show a significant difference in term of difference during the SRT with respect to patients with post‐SRTBF. Starting from this difference, we identified a simple and practical analytical method for the post‐SRTBF prediction before the end of the SRT. The data corresponds with the model and the analytical method is highly predictive (Sensitivity = 81%, Specificity = 85%, Accuracy = 83%).ConclusionThis study offers a new tool to early predict Pca relapse overtime and to select patients who can benefit from an early additional systemic treatment.

Field tests of micro screw anchor piles under different loading conditions at three soil sites

Micro screw anchor piles are ideal as the foundation of solar power plants and have been widely used in recent years. In this study, test apparatuses for the uplift loading test, compression test, and lateral loading test of the micro screw anchor pile were designed. Comprehensive field tests (totally 72 piles) for six types of micro screw anchor pile were conducted in clayey, loess, and sandy sites, and some conclusions can be drawn as follows. (1) The typical ranges of bearing capacity in three soil sites with the micro screw anchor pile were obtained under uplift, compressive, and lateral loading conditions; the lateral bearing capacity varied over a small range (8.1 to 11.7 kN) for various site conditions and pile configurations and was much smaller than the uplift and compressive bearing capacities. (2) The screw anchor pile with more helical blades had better performance under uplift, compressive, and lateral loading conditions; increasing the embedded depth of blade substantially enhanced the uplift and compressive bearing capacities but only slightly influenced the lateral bearing capacity. (3) The clayey site had larger uplift and compressive bearing capacities than the loess and sandy sites; the uplift bearing capacity was larger than the compressive bearing capacity in clay but smaller than the compressive bearing capacity in sand. (4) The classic cylindrical shear method was improved by taking into account the shaft resistance above and below the blade(s), which can better predict the uplift and compressive bearing capacities; as for the lateral bearing capacity, a simple but practical prediction method was also proposed. This study will contribute to a better understanding of the failure mechanism and bearing capacity of micro screw anchor piles. The site information and test results are promising to provide data for further analytical and numerical works.

Energy-Based Prediction of the Displacement of DCFP Bearings

Isolation systems are currently being widely applied for earthquake resistance. During the design stage for such systems, the displacement response and input energy of the isolation layer are two of the main concerns. The prediction of these values is also of vital importance during the early stages of the structural design. In this study, the simple prediction method of double concave friction pendulum (DCFP) bearings is proposed, which can relate the response displacement of the isolation layer to the ground velocity through energy transfer with sufficient accuracy. Two friction models (the precise and simplified model) and a constant friction coefficient of double concave friction pendulum (DCFP) bearings are comprehensively validated by full-scale sinusoidal dynamic tests under various conditions. In addition, a response analysis, based on previous studies, was conducted using the friction models under selected unidirectional earthquake excitations, and the accuracy of using the simplified model in the response analysis was verified. Based on the response analysis data, this article verifies and optimizes the proposed prediction method by parameterizing the characteristics of earthquakes and combining the energy balance in order to gain a deeper understanding of the design of the isolation systems.

Experimental and numerical study on static behaviour of uniplanar CFDST-CHS T-joints

Experimental and numerical study on static behaviour of uniplanar T-joints between circular concrete-filled double-skin steel tube (CFDST) chord and circular hollow section (CHS) brace, i.e., CFDST-CHS T-joints for short, are presented in this paper. Twelve uniplanar T-joint specimens, including 10 joints with circular CFDST chords and 2 reference ones with CHS chords, were tested under specified boundary conditions. The experimental parameters included hollow ratio of the chord (χ), brace to chord diameter ratio (β) and axial compression ratio of the chord (n). The failure patterns, load versus displacement (strain) curves and bearing capacities of the specimens were collected and analysed. The composite tubular T-joint specimens showed better structural performance than the reference steel ones. For the CFDST-CHS T-joint specimens, the failure patterns change from the local buckling of CHS brace to the compression-bending failure of CFDST chord with the increase of β. With the local buckling failure of CHS brace, the bearing capacity of the composite tubular T-joint specimens increases with the growing of χ and β. However, with the compression-bending failure of CFDST chord, the bearing capacity of the composite tubular T-joint specimens increases with the rising of n. Advanced numerical simulation on the structural performance of uniplanar CFDST-CHS T-joints was implemented using finite element analysis (FEA) method, and the accuracy of the predicted responses was evaluated against the experimental results. Finally, based on the FEA results, the simplified method for the bearing capacity prediction of the composite tubular T-joints was proposed.

Crop Photosynthetic Performance Monitoring Based on a Combined System of Measured and Modelled Chloroplast Electron Transport Rate in Greenhouse Tomato.

Combining information of plant physiological processes with climate control systems can improve control accuracy in controlled environments as greenhouses and plant factories. Through that, resource optimization can be achieved. To predict the plant physiological processes and implement them in control actions of interest, a reliable monitoring system and a capable control system are needed. In this paper, we focused on the option to use real-time crop monitoring for precision climate control in greenhouses. For that, we studied the processes and external factors influencing leaf net CO2 assimilation rate (AL, µmol CO2 m-2 s-1) as possible variables of a plant performance indicator. While measured greenhouse environmental variables such as light, temperature, or humidity showed a direct relation between AL and light-quantum yield of photosystem II (Φ2), we defined three objectives: (1) to explore the relationship between climate variables and AL, as well as Φ2; (2) create a simple and reliable method for real‐time prediction of AL with continuously Φ2 measurements; and (3) calibrate parameters to predict chloroplast electron transport rate as input in AL modelling. Due to practical obstacles in measuring CO2 gas-exchange in commercial production, we explored a method to predict AL by measuring Φ2 of leaves in a commercial hydroponic greenhouse tomato crop (“Pureza”). We calculated AL with two different approaches based on either the negative exponential response model with simplified biochemical equations (marked as Model I) or the non-rectangular hyperbola full biochemical photosynthetic models (marked as Model II). Using Model I can only be used to predict AL with large uncertainty (R2 0.64; RMSE 2.21), while using Φ2 as input to Model II could be used to improve the prediction accuracy of AL (R2 0.71; RMSE 1.98). Our results suggests that (1) Φ2 light signals can be used to predict net photosynthesis rate with high accuracy; (2) a parameterized photosynthetic electron transport rate model is suitable predicting measured electron transport rate (J) and AL. The system can be used as decision support system (DSS) for plant and crop performance monitoring when leaf-dynamics are up-scaled to the plant or crop level.

Simple Method for Prediction of Undrained Shear Strength of Saturated Soil Subjected to Cyclic Loading

Simple Method for Prediction of Undrained Shear Strength of Saturated Soil Subjected to Cyclic Loading

A Simplified Method for Real-Time Prediction of Temperature in Mass Concrete at Early Age

According to the practice of temperature control in dam concrete, within a few days after a concrete block is poured, the temperature at the core of the concrete rises rapidly. The maximum temperature may still exceed the standard even under a relatively perfect post-cooling system, which is mostly caused by failure to quickly and correctly judge the development of the early-age temperature. This study investigates concrete temperature at an early age via in situ monitoring data collected from Baihetan arch dam and Wudongde arch dam. A simplified algorithm of temperature prediction is formed, which only considers the heat released by cement hydration and the cooling effect of cooling pipes. The influence of a cooling pipe on the measuring point of the thermometer is investigated, and a simple empirical formula to calculate the cooling effect is obtained. An equation for the rate of hydration temperature rise is achieved by combining measured data and the formula used to calculate the cooling effect. Furthermore, through the explorations of the related data, it is determined that the cement hydration ratio of the two dams is quite low during concreting. On the basis of the data collected from the field, the method to predict temperature proposed in this study is tested and proven.

A simple new scoring system for predicting the mortality of severe acute pancreatitis: A retrospective clinical study.

It is critical to accurately identify patients with severe acute pancreatitis (SAP) in a timely manner. This study aimed to develop a new simplified AP scoring system based on data from Chinese population.We retrospectively analyzed a consecutive series of 585 patients diagnosed with SAP at the Changhai hospital between 2009 and 2017. The new Chinese simple scoring system (CSSS) was derived using logistic regression analysis and was validated in comparison to 4 existing systems using receiver operating characteristic curves.Six variables were selected for incorporation into CSSS, including serum creatinine, blood glucose, lactate dehydrogenase, heart rate, C-reactive protein, and extent of pancreatic necrosis. The new CSSS yields a maximum total score of 9 points. The cut-offs for predicting mortality and severity (discriminating moderately SAP from SAP) were set as 6 points and 4 points respectively. Compared with 4 existing scoring systems, the area under the receiver operating characteristic of CSSS for prediction of mortality was 0.838, similar to acute physiology and chronic health evaluation II (0.844) and higher than Ranson's score (0.702, P < .001), bedside index of severity in acute pancreatitis (0.615), and modified computed tomography severity index (MCTSI) (0.736). For predicting SAP severity, CSSS was the most accurate (0.834), followed by acute physiology and chronic health evaluation II (0.800), Ranson's score (0.702), MCTSI (0.660), and bedside index of severity in acute pancreatitis (0.570). Further, the accuracy of predicting pancreatic infection with CSSS was the highest (0.634), similar to that of MCTSI (0.641).A new prognostic scoring system for SAP was derived and validated in a Chinese sample. This scoring system is a simple and accurate method for prediction of mortality.

A Simple and Sustainable Prediction Method of Liquefaction-Induced Settlement at Pohang Using an Artificial Neural Network

Conventionally, liquefaction-induced settlements have been predicted through numerical or analytical methods. In this study, a machine learning approach for predicting the liquefaction-induced settlement at Pohang was investigated. In particular, we examined the potential of an artificial neural network (ANN) algorithm to predict the earthquake-induced settlement at Pohang on the basis of standard penetration test (SPT) data. The performance of two ANN models for settlement prediction was studied and compared in terms of the R2 correlation. Model 1 (input parameters: unit weight, corrected SPT blow count, and cyclic stress ratio (CSR)) showed higher prediction accuracy than model 2 (input parameters: depth of the soil layer, corrected SPT blow count, and the CSR), and the difference in the R2 correlation between the models was about 0.12. Subsequently, an optimal ANN model was used to develop a simple predictive model equation, which was implemented using a matrix formulation. Finally, the liquefaction-induced settlement chart based on the predictive model equation was proposed, and the applicability of the chart was verified by comparing it with the interferometric synthetic aperture radar (InSAR) image.

Air Quality Prediction Based on Neural Network Model of Long Short-term Memory

Air pollution is a serious environmental problem, which has caused more and more concern all over the world. At present, research focuses on air quality prediction, usually using the following two methods: deterministic method and statistical method. Due to the unreliability of pollutant emission data and incomplete theoretical basis, the prediction accuracy of simulation results of these two methods is low, and these methods do not take into account the problem of time loss. In order to solve the problems of low prediction accuracy, low efficiency, and missing time factors in current air quality prediction research, a simple air quality prediction method LSTM neural network model is proposed. According to the characteristics of time series, the problem of multiple input time variables can be well solved. The datasets in two cities respectively prove that LSTM neural network model can effectively improve the accuracy of air quality prediction. The model can also be used to predict other multivariable input time series. Although air quality prediction based on LSTM presented in this paper has achieved the expected goal, there is still room for improvement in considering both spatial correlation and temporal dimension.

Simplified Predictive Duty Cycle Control of Multilevel Converters With Internal Identical Structure

Classical finite control set model predictive control of multilevel converters faces two major limitations: heavy computation burden and low steady-state performance. On the other hand, the internal paralleled or cascaded identical structure is extensively employed in multilevel converters as the voltage level increases. Taking advantage of the internal identical structure in multilevel converters, a simplified predictive duty cycle control method for multilevel converters is proposed in this article. First, a series of switching pairs with the identical structure are modeled as a virtual switching state, and the number of the corresponding voltage vectors can be significantly reduced. Then, a least-square optimization problem is formulated to calculate the duty cycles for optimal output current tracking using these virtual voltage vectors. The output current quality is enhanced due to the employment of the interleaved output voltage stage with phase-shifted pulsewidth modulation. As the number of voltage vectors is significantly reduced, the computation burden is drastically reduced. The proposed method can be applied in various multilevel topologies with internal identical structures. Simulation and experimental results on the multilevel converters with internal cascaded/paralleled identical cells are presented to verify the good performance and application value of the proposed method.

Non-destructive analysis of the moisture content in shiitake mushrooms (Lentinula edodes) using near-infrared imaging at 1450 nm

Moisture content (MC) is an important factor in the freshness of shiitake mushrooms (Lentinula edodes). In this study, we developed a simple prediction method for MC of shiitake mushrooms using near-infrared (NIR) imaging at 1450 nm, as the band at 1450 nm is strongly correlated with OH groups. First, a NIR image was captured by an InGaAs sensor with a bandpass filter of 1450 nm; the captured image was then calibrated using an image taken at 1100 nm to remove shade and shape effects of mushrooms. The actual MC of shiitake mushrooms was measured using an oven-drying method, and a calibration curve was obtained for the cap surface and the gill side, respectively. The correlation coefficient reached more than 0.85, and the standard error of calibration ranged from 3.1% to 8.2%. Non-destructive MC images were quantified using the constructed calibration curves, and the experimental results showed that differences in the water-holding property can be visualized over 6 h for each side using our method.

Statistical models to predict recruitment in clinical trials were rarely used by statisticians in UK and European networks

ObjectiveIdentify the current practice for recruitment prediction and monitoring within clinical trials. Study Design and SettingChief investigators (CIs) were surveyed to identify data sources and adjustments made to support recruitment prediction. Statisticians were surveyed to determine methods and adjustments used when predicting and monitoring recruitment. Participants were identified from the National Institute for Health Research recently funded studies, the UK Clinical Research Collaboration registered Clinical Trial Units network or by the European Clinical Research Infrastructure Network. ResultsA total of 51 CIs (UK = 32, ECRIN = 19) and 104 statisticians (UK = 51, ECRIN = 53) were contacted. Response rates varied (CIs UK = 53% ECRIN = 32%; statisticians UK = 98% ECRIN = 36%).Multiple data sources are used to support recruitment rates, most commonly audit data from multiple sites. Variation in individual site recruitment rates are frequently incorporated, but staggered site openings were featured more commonly among UK respondents. Simple prediction methods are preferred to rarely used statistical models. Lack of familiarity with statistical methods are barriers to their use with evidence needed to justify the time required to support their implementation. ConclusionSimplistic methods will continue as the mainstay of prediction; however, generation of evidence supporting the benefits of complex statistical models should promote their implementations. Multiple data sources to support recruitment prediction are being used, and further work on the quality of these data is needed. Pressure to be optimistic about recruitment rates for the trial to be attractive to funders was felt by a sizable minority.

Assessment of the G-ROP study criteria for predicting retinopathy of prematurity: results from a tertiary centre in Turkey.

The postnatal growth and retinopathy of prematurity (G-ROP) study has proposed a new model for retinopathy of prematurity (ROP) prediction based on gestational age, birth weight and postnatal weight gain. The purpose of the current study is to assess the efficacy of the G-ROP modelfor predicting ROP among a Turkish cohort of premature infants. Records of infants who underwent ROP screening examinations between 2012 and 2019 were reviewed retrospectively. Infants with a documented ROP outcome and regular body weight measurements until day 40 of life were included. The G-ROP model was applied to the study group. The outcome measures were sensitivity and specificity of the model for detecting any stage ROP and treated ROP. A total of 242 infants were included. The G-ROP model identified 168 infants to be screened for ROP. The sensitivity was 88.3% for detecting any stage ROP and 91.2% for treated ROP. The specificity for any stage ROP and treated ROP was 51.7% and 34.1%, respectively. The incorporation of bronchopulmonary dysplasia to the model increased the sensitivity to 100% with 22.7% reduction in the number of screened infants. The G-ROP model is a simple and effective method for ROP prediction. However, in the current cohort, a small number of infants requiring treatment would be missed if G-ROP criteria were applied. The model may be modified with addition of bronchopulmonary dysplasia to detect all treatment requiring cases. Prospective studies among larger groups are necessary to assess the applicability of the modified model.